Controlling Allosteric Networks in Proteins

Nucleic acid nanobiosystems for cancer theranostics: an overview of emerging trends and challenges

Different cancers remain major global health challenges due to their diverse biological behaviors and significant treatment hurdles. The aging of populations and lifestyle factors increase cancer occurrence and place increasing pressure on healthcare systems. Despite continuous advancements, many cancers remain fatal due to late-stage diagnosis, tumor heterogeneity, and drug resistance, thus necessitating urgent development of innovative treatment solutions. Therapeutic nucleic acids, a new class of biological drugs, offer a promising approach to overcoming these challenges. The recent Nucleic Acids and Nanobiosystems in Cancer Theranostics (NANCT) conference brought together internationally recognized experts from 15 countries to discuss cutting-edge research, spanning from oncolytic viruses to anticancer RNA nanoparticles and other emerging nanotechnologies. This review captures key insights and developments, emphasizing the need for interdisciplinary translation of scientific advancements into clinical practice and shaping the future of personalized cancer treatments for improved therapeutic outcomes.

Mechanism of influence of nattokinase terminal sequence on catalytic performance and molecular modification

Nattokinase from traditional food natto has the potential to be a thrombolytic agent. Its terminal sequence has an important effect on the catalytic performance of nattokinase, but the specific mechanism is still unclear. In this study, computational simulation combined with truncated mutagenesis and alanine scanning technology were used to identify the key sites affecting the catalytic performance of nattokinase. Subsequently, mutants Q10L and Q275G with 4.00-fold and 4.83-fold increased half-lives at 55 °C were screened by site-directed saturation mutagenesis. Constraint network analysis and molecular dynamics simulation revealed that the thermal stability of the two mutants was enhanced by solvent interaction and indirect effects on the Ca2+ binding sites. However, the combined mutation Q10L-Q275G did not demonstrate additive thermal stability. This study provides technical support for the engineering modification of nattokinase.

Dynamic Allostery: Evolution's Double-Edged Sword in Protein Function and Disease

Allostery is a core mechanism in biology that allows proteins to communicate and regulate activity over long structural distances. While classical models of allostery focus on conformational changes triggered by ligand binding, dynamic allostery-where protein function is modulated through alterations in thermal fluctuations without major conformational shifts-has emerged as a critical evolutionary mechanism. This review explores how evolution leverages dynamic allostery to fine-tune protein function through subtle mutations at distal sites, preserving core structural architecture while dramatically altering functional properties. Using a combination of computational approaches including Dynamic Flexibility Index (DFI), Dynamic Coupling Index (DCI), and vibrational density of states (VDOS) analysis, we demonstrate that functional adaptations in proteins often involve "hinge-shift" mechanisms, where redistribution of rigid and flexible regions modulates collective motions without changing the overall fold. This evolutionary principle is a double-edged sword: the same mechanisms that enable functional innovation also create vulnerabilities that can be exploited in disease states. Disease-associated variants frequently occur at positions highly coupled to functional sites despite being physically distant, forming Dynamic Allosteric Residue Couples (DARC sites). We demonstrate applications of these principles in understanding viral evolution, drug resistance, and capsid assembly dynamics. Understanding dynamic allostery provides critical insights into protein evolution and offers new avenues for therapeutic interventions targeting allosteric regulation.

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions

Machine learning (ML) techniques have been making major impacts on all areas of science and engineering, including biophysics. In this review, we discuss several applications of ML to biophysical problems based on our recent research. The topics include the use of ML techniques to identify hotspot residues in allosteric proteins using deep mutational scanning data and to analyze how mutations of these hotspots perturb co-operativity in the framework of a statistical thermodynamic model, to improve the accuracy of free energy simulations by integrating data from different levels of potential energy functions, and to determine the phase transition temperature of lipid membranes. Through these examples, we illustrate the unique value of ML in extracting patterns or parameters from complex data sets, as well as the remaining limitations. By implementing the ML approaches in the context of physically motivated models or computational frameworks, we are able to gain a deeper mechanistic understanding or better convergence in numerical simulations. We conclude by briefly discussing how the introduced models can be further expanded to tackle more complex problems.

Investigation of serotonin-receptor interactions, stability and signal transduction pathways via molecular dynamics simulations

Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT1B receptor (5HT1BR) via electrostatic interactions. Key residues for electrostatic interactions were identified via bond distance analysis and frustration analysis methods. An end-point free energy calculation method determines the stability of the 5-HT1BR due to serotonin binding. The single-point mutation of the polar/charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the quantitative contribution of these residues to the stability of the serotonin-receptor complex. The principal component analysis reflects that the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor regarding dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembranes and mini-Go, which indicates that the signal transduction happens between mini-Go and the receptor. Allosteric pathway analysis reveals the key nodes and key pathways for signal transduction in 5-HT1BR. These results provide useful insights into the study of signal transduction pathways and mutagenesis to regulate the binding and functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

An FDA-approved drug structurally and phenotypically corrects the K210del mutation in genetic cardiomyopathy models

Dilated cardiomyopathy (DCM) due to genetic disorders results in decreased myocardial contractility, leading to high morbidity and mortality rates. There are several therapeutic challenges in treating DCM, including poor understanding of the underlying mechanism of impaired myocardial contractility and the difficulty of developing targeted therapies to reverse mutation-specific pathologies. In this report, we focused on K210del, a DCM-causing mutation, due to 3-nucleotide deletion of sarcomeric troponin T (TnnT), resulting in loss of Lysine210. We resolved the crystal structure of the troponin complex carrying the K210del mutation. K210del induced an allosteric shift in the troponin complex resulting in distortion of activation Ca2+-binding domain of troponin C (TnnC) at S69, resulting in calcium discoordination. Next, we adopted a structure-based drug repurposing approach to identify bisphosphonate risedronate as a potential structural corrector for the mutant troponin complex. Cocrystallization of risedronate with the mutant troponin complex restored the normal configuration of S69 and calcium coordination. Risedronate normalized force generation in K210del patient-induced pluripotent stem cell-derived (iPSC-derived) cardiomyocytes and improved calcium sensitivity in skinned papillary muscles isolated from K210del mice. Systemic administration of risedronate to K210del mice normalized left ventricular ejection fraction. Collectively, these results identify the structural basis for decreased calcium sensitivity in K210del and highlight structural and phenotypic correction as a potential therapeutic strategy in genetic cardiomyopathies.

Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses

Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include extensive atomistic simulations and free energy computations for different functional states of TetR, machine learning analysis of the DMS data and a statistical thermodynamic model for the experimental induction data for the WT protein and a handful of hotspot mutants. Collectively, these computations provided insights into the structural and energetic basis of allostery in TetR, and the distinct contributions of allostery hotspots. The results highlight that the allostery function (i.e., the induction activity) of TetR can be modulated by perturbing both inter-domain coupling and intra-domain properties, such as the population of the binding-competent conformation of each domain. This mechanistic degeneracy qualitatively explains the broad distribution of allostery hotspots across the protein structure observed in the DMS experiments, and also informs the design of strategies aimed at identifying allostery hotspots. The mechanistic framework and the multi-faceted computational approaches are expected to be applicable to the analysis of other allostery systems, especially those sharing the similar two-domain structural topology, and to the design of allostery modulators.

Exploring the constitutive activation mechanism of the class A orphan GPR20

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Autonomous Nucleic Acid and Protein Nanocomputing Agents Engineered to Operate in Living Cells

In recent years, the rapid development and employment of autonomous technology have been observed in many areas of human activity. Autonomous technology can readily adjust its function to environmental conditions and enable an efficient operation without human control. While applying the same concept to designing advanced biomolecular therapies would revolutionize nanomedicine, the design approaches to engineering biological nanocomputing agents for predefined operations within living cells remain a challenge. Autonomous nanocomputing agents made of nucleic acids and proteins are an appealing idea, and two decades of research has shown that the engineered agents act under real physical and biochemical constraints in a logical manner. Throughout all domains of life, nucleic acids and proteins perform a variety of vital functions, where the sequence-defined structures of these biopolymers either operate on their own or efficiently function together. This programmability and synergy inspire massive research efforts that utilize the versatility of nucleic and amino acids to encode functions and properties that otherwise do not exist in nature. This Perspective covers the key concepts used in the design and application of nanocomputing agents and discusses potential limitations and paths forward.

Graph theory approaches for molecular dynamics simulations

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

Allosteric Communication Mediated by Protein Contact Clusters: A Dynamical Model

Describing the puzzling phenomenon of long-range communication between distant protein sites, allostery is of paramount importance in biomolecular regulation and signal transduction. It is commonly assumed to arise from a conformational rearrangement of the protein, although the underlying dynamical process has remained largely elusive. This study introduces a dynamical model of allosteric communication based on "contact clusters"─localized groups of highly correlated contacts that facilitate interactions between secondary structures. The model shows that allostery involves a multistep process with cooperative contact changes within clusters and communication between distant clusters mediated by rigid secondary structures. Considering time-dependent experiments on a photoswitchable PDZ3 domain, extensive (in total ∼500 μs) molecular dynamics simulations are conducted that directly monitor the photoinduced allosteric transition. The structural reorganization is illustrated by the time evolution of the contact clusters and the ligand, which effects the nonlocal coupling between distant clusters. A time scale analysis reveals dynamics from nano- to microseconds, which are in excellent agreement with the experimentally measured time scales. While the simulation of larger systems may require enhanced sampling techniques, it is expected that the general picture of allostery mediated by communicating contact clusters will still be applicable.

Optogenetically engineered Septin-7 enhances immune cell infiltration of tumor spheroids

Chimeric antigen receptor T cell therapies have achieved great success in eradicating some liquid tumors, whereas the preclinical results in treating solid tumors have proven less decisive. One of the principal challenges in solid tumor treatment is the physical barrier composed of a dense extracellular matrix, which prevents immune cells from penetrating the tissue to attack intratumoral cancer cells. Here, we improve immune cell infiltration into solid tumors by manipulating septin-7 functions in cells. Using protein allosteric design, we reprogram the three-dimensional structure of septin-7 and insert a blue light-responsive light-oxygen-voltage-sensing domain 2 (LOV2), creating a light-controllable septin-7-LOV2 hybrid protein. Blue light inhibits septin-7 function in live cells, inducing extended cell protrusions and cell polarization, enhancing cell transmigration efficiency through confining spaces. We genetically edited human natural killer cell line (NK92) and mouse primary CD8+ T-cells expressing the engineered protein, and we demonstrated improved penetration and cytotoxicity against various tumor spheroid models. Our proposed strategy to enhance immune cell infiltration is compatible with other methodologies and therefore, could be used in combination to further improve cell-based immunotherapies against solid tumors.

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in the study of protein dynamics. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in the dynamics. For example, specific mutations on the Met20 loop in Escherichia coli DHFR (N23PP/S148A) are known to disrupt millisecond-time scale motions as well as reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues that possesses increased correlated motions. We then go on to show that allosteric communication in this network is knocked down in N23PP/S148A mutant E. coli DHFR. We also identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline motif. Finally, we demonstrate a concerted evolutionary change in the human DHFR allosteric networks, which maintains dynamic communication. These findings strongly implicate protein dynamics as a driving force for evolution.

Dimerization Promotes PKR Activation by Modulating Energetics of αC Helix Conversion between Active and Inactive Conformations

Protein kinase R (PKR) functions in the eukaryotic innate immune system as a first-line defense against viral infections. PKR binds viral dsRNA, leading to autophosphorylation and activation. In its active state, PKR can phosphorylate its primary substrate, eIF2α, which blocks the initiation of translation in the infected cell. It has been established that PKR activation occurs when the kinase domain dimerizes in a back-to-back configuration. However, the mechanism by which dimerization leads to enzymatic activation is not fully understood. Here, we investigate the structural mechanistic basis and energy landscape for PKR activation, with a focus on the αC helix─a kinase activation and signal integration hub─using all-atom equilibrium and enhanced sampling molecular dynamics simulations. By employing window-exchange umbrella sampling, we compute free-energy profiles of activation, which show that back-to-back dimerization stabilizes a catalytically competent conformation of PKR. Key hydrophobic residues in the homodimer interface contribute to stabilization of the αC helix in an active conformation and the position of its critical glutamate residue. Using linear mutual information analysis, we analyze allosteric communication connecting the protomers' N-lobes and the αC helix dimer interface with the αC helix.

Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking

The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

Allosteric Control of the Catalytic Properties of Dipeptide-Based Supramolecular Assemblies

Allostery, as seen in extant biology, governs the activity regulation of enzymes through the redistribution of conformational equilibria upon binding an effector. Herein, a minimal design is demonstrated where a dipeptide can exploit dynamic imine linkage to condense with simple aldehydes to access spherical aggregates as catalytically active states, which facilitates an orthogonal reaction due to the closer proximity of catalytic residues (imidazoles). The allosteric site (amine) of the minimal catalyst can concomitantly bind to an inhibitor via a dynamic exchange, which leads to the alternation of the energy landscape of the self-assembled state, resulting in downregulation of catalytic activity. Further, temporal control over allosteric regulation is realized via a feedback-controlled autonomous reaction network that utilizes the hydrolytic activity of the (in)active state as a function of time.

Allosteric pathways of SARS and SARS-CoV-2 spike protein identified by neural relational inference

The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein must undergo a crucial conformational transition to invade human cells. It is intriguing that this transition is accompanied by a synchronized movement of the entire spike protein. Therefore, it is possible to design allosteric regulators targeting non-RBD but hindering the conformational transition of RBD. To understand the allosteric mechanism in detail, we establish a computational framework by integrating coarse-grained molecular dynamic simulations and a state-of-the-art neural network model called neural relational inference. Leveraging this framework, we have elucidated the allosteric pathway of the SARS-CoV-2 spike protein at the residue level and identified the molecular mechanisms involved in the transmission of allosteric signals. The movement of D614 is coupled with that of Q321. This interaction subsequently influences the movement of K528/K529, ultimately coupling with the movement of RBD during conformational changes. Mutations that weaken the interactions within this pathway naturally block the allosteric signal transmission, thereby modulating the conformational transitions. This observation also offers a rationale for the distinct allosteric patterns observed in the SARS-CoV spike protein. Our result provides a useful method for analyzing the dynamics of potential viral variants in the future.

Mining the Dynamical Properties of Substrate and FAD Binding Pockets of LSD1: Hints for New Inhibitor Design Direction

Lysine-specific demethylase 1 (LSD1), a highly sophisticated epigenetic regulator, orchestrates a range of critical cellular processes, holding promising therapeutic potential for treating diverse diseases. However, the clinical research progress targeting LSD1 is very slow. After 20 years of research, only one small-molecule drug, BEA-17, targeting the degradation of LSD1 and CoREST has been approved by the U.S. Food and Drug Administration. The primary reason for this may be the lack of abundant structural data regarding its intricate functions. To gain a deeper understanding of its conformational dynamics and guide the drug design process, we conducted molecular dynamics simulations to explore the conformational states of LSD1 in the apo state and under the influence of cofactors of flavin adenine dinucleotide (FAD) and CoREST. Our results showed that, across all states, the substrate binding pocket exhibited high flexibility, whereas the FAD binding pocket remained more stable. These distinct dynamical properties are essential for LSD1's ability to bind various substrates while maintaining efficient demethylation activity. Both pockets can be enlarged by merging with adjacent pockets, although only the substrate binding pocket can shrink into smaller pockets. These new pocket shapes can inform inhibitor design, particularly for selectively FAD-competitive inhibitors of LSD1, given the presence of numerous FAD-dependent enzymes in the human body. More interestingly, in the absence of FAD binding, the united substrate and FAD binding pocket are partitioned by the conserved residue of Tyr761, offering valuable insights for the design of inhibitors that disrupt the crucial steric role of Tyr761 and the redox role of FAD. Additionally, we identified pockets that positively or negatively correlate with the substrate and FAD binding pockets, which can be exploited for the design of allosteric or concurrent inhibitors. Our results reveal the intricate dynamical properties of LSD1 as well as multiple novel conformational states, which deepen our understanding of its sophisticated functions and aid in the rational design of new inhibitors.

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

Nano-optogenetics for Disease Therapies

Optogenetic, known as the method of 21 centuries, combines optic and genetic engineering to precisely control photosensitive proteins for manipulation of a broad range of cellular functions, such as flux of ions, protein oligomerization and dissociation, cellular intercommunication, and so on. In this technique, light is conventionally delivered to targeted cells through optical fibers or micro light-emitting diodes, always suffering from high invasiveness, wide-field illumination facula, strong absorption, and scattering by nontargeted endogenous substance. Light-transducing nanomaterials with advantages of high spatiotemporal resolution, abundant wireless-excitation manners, and easy functionalization for recognition of specific cells, recently have been widely explored in the field of optogenetics; however, there remain a few challenges to restrain its clinical applications. This review summarized recent progress on light-responsive genetically encoded proteins and the myriad of activation strategies by use of light-transducing nanomaterials and their disease-treatment applications, which is expected for sparking helpful thought to push forward its preclinical and translational uses.

Dynamics-driven allosteric stimulation of diguanylate cyclase activity in a red light-regulated phytochrome

Sensor-effector proteins integrate information from different stimuli and transform this into cellular responses. Some sensory domains, like red-light responsive bacteriophytochromes, show remarkable modularity regulating a variety of effectors. One effector domain is the GGDEF diguanylate cyclase catalyzing the formation of the bacterial second messenger cyclic-dimeric-guanosine monophosphate. While critical signal integration elements have been described for different phytochromes, a generalized understanding of signal processing and communication over large distances, roughly 100 Å in phytochrome diguanylate cyclases, is missing. Here we show that dynamics-driven allostery is key to understanding signal integration on a molecular level. We generated protein variants stabilized in their far-red-absorbing Pfr state and demonstrated by analysis of conformational dynamics using hydrogen-deuterium exchange coupled to mass spectrometry that single amino acid replacements are accompanied by altered dynamics of functional elements throughout the protein. We show that the conformational dynamics correlate with the enzymatic activity of these variants, explaining also the increased activity of a non-photochromic variant. In addition, we demonstrate the functional importance of mixed Pfr/intermediate state dimers using a fast-reverting variant that still enables wild-type-like fold-changes of enzymatic stimulation by red light. This supports the functional role of single protomer activation in phytochromes, a property that might correlate with the non-canonical mixed Pfr/intermediate-state spectra observed for many phytochrome systems. We anticipate our results to stimulate research in the direction of dynamics-driven allosteric regulation of different bacteriophytochrome-based sensor-effectors. This will eventually impact design strategies for the creation of novel sensor-effector systems for enriching the optogenetic toolbox.

Photoswitchable Cascades for Allosteric and Bidirectional Control over Covalent Bonds and Assemblies

Studies of complex systems and emerging properties to mimic biosystems are at the forefront of chemical research. Dynamic multistep cascades, especially those exhibiting allosteric regulation, are challenging. Herein, we demonstrate a versatile platform of photoswitchable covalent cascades toward remote and bidirectional control of reversible covalent bonds and ensuing assemblies. The relay of a photochromic switch, keto-enol equilibrium, and ring-chain equilibrium allows light-mediated reversible allosteric structural changes. The accompanying distinct reactivity further enables photoswitchable dynamic covalent bonding and release of substrates bidirectionally through alternating two wavelengths of light, essentially realizing light-mediated signaling cycles. The downfall of energy by covalent bond formation/scission upon photochemical reactions offers the driving force for the controlled direction of the cascade. To show the molecular diversity, photoswitchable on-demand assembly/disassembly of covalent polymers, including structurally reconfigurable polymers, was realized. This work achieves photoswitchable allosteric regulation of covalent architectures within dynamic multistep cascades, which has rarely been reported before. The results resemble allosteric control within biological signaling networks and should set the stage for many endeavors, such as dynamic assemblies, molecular motors, responsive polymers, and intelligent materials.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Variance sum rule for entropy production

Entropy production is the hallmark of nonequilibrium physics, quantifying irreversibility, dissipation, and the efficiency of energy transduction processes. Despite many efforts, its measurement at the nanoscale remains challenging. We introduce a variance sum rule (VSR) for displacement and force variances that permits us to measure the entropy production rate σ in nonequilibrium steady states. We first illustrate it for directly measurable forces, such as an active Brownian particle in an optical trap. We then apply the VSR to flickering experiments in human red blood cells. We find that σ is spatially heterogeneous with a finite correlation length, and its average value agrees with calorimetry measurements. The VSR paves the way to derive σ using force spectroscopy and time-resolved imaging in living and active matter.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study, we employ this paradigm to answer a basic question: in enzyme superfamilies, where the catalytic mechanism, active sites, and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as representatives of the conserved protein tyrosine phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of the catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

Creation of a point-of-care therapeutics sensor using protein engineering, electrochemical sensing and electronic integration

Point-of-care sensors, which are low-cost and user-friendly, play a crucial role in precision medicine by providing quick results for individuals. Here, we transform the conventional glucometer into a 4-hydroxytamoxifen therapeutic biosensor in which 4-hydroxytamoxifen modulates the electrical signal generated by glucose oxidation. To encode the 4-hydroxytamoxifen signal within glucose oxidation, we introduce the ligand-binding domain of estrogen receptor-alpha into pyrroloquinoline quinone-dependent glucose dehydrogenase by constructing and screening a comprehensive protein insertion library. In addition to obtaining 4-hydroxytamoxifen regulatable engineered proteins, these results unveil the significance of both secondary and quaternary protein structures in propagation of conformational signals. By constructing an effective bioelectrochemical interface, we detect 4-hydroxytamoxifen in human blood samples as changes in the electrical signal and use this to develop an electrochemical algorithm to decode the 4-hydroxytamoxifen signal from glucose. To meet the miniaturization and signal amplification requirements for point-of-care use, we harness power from glucose oxidation to create a self-powered sensor. We also amplify the 4-hydroxytamoxifen signal using an organic electrochemical transistor, resulting in milliampere-level signals. Our work demonstrates a broad interdisciplinary approach to create a biosensor that capitalizes on recent innovations in protein engineering, electrochemical sensing, and electrical engineering.

Conflicting Interfacial Electrostatic Interactions as a Design Principle to Modulate Long-Range Interdomain Communication

The extent and molecular basis of interdomain communication in multidomain proteins, central to understanding allostery and function, is an open question. One simple evolutionary strategy could involve the selection of either conflicting or favorable electrostatic interactions across the interface of two closely spaced domains to tune the magnitude of interdomain connectivity. Here, we study a bilobed domain FF34 from the eukaryotic p190A RhoGAP protein to explore one such design principle that mediates interdomain communication. We find that while the individual structural units in wild-type FF34 are marginally coupled, they exhibit distinct intrinsic stabilities and low cooperativity, manifesting as slow folding. The FF3-FF4 interface harbors a frustrated network of highly conserved electrostatic interactions-a charge troika-that promotes the population of multiple, decoupled, and non-native structural modes on a rugged native landscape. Perturbing this network via a charge-reversal mutation not only enhances stability and cooperativity but also dampens the fluctuations globally and speeds up the folding rate by at least an order of magnitude. Our work highlights how a conserved but nonoptimal network of interfacial electrostatic interactions shapes the native ensemble of a bilobed protein, a feature that could be exploited in designing molecular systems with long-range connectivity and enhanced cooperativity.

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Unveiling the RNA-mediated allosteric activation discloses functional hotspots in CRISPR-Cas13a

Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. It utilizes a CRISPR RNA (crRNA) to target RNA sequences and trigger a composite active site formed by two 'Higher Eukaryotes and Prokaryotes Nucleotide' (HEPN) domains, cleaving any solvent-exposed RNA. In this system, an intriguing form of allosteric communication controls the RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory to decipher this intricate activation mechanism. We show that the binding of a target RNA acts as an allosteric effector, by amplifying the communication signals over the dynamical noise through interactions of the crRNA at the buried HEPN1-2 interface. By introducing a novel Signal-to-Noise Ratio (SNR) of communication efficiency, we reveal critical allosteric residues-R377, N378, and R973-that rearrange their interactions upon target RNA binding. Alanine mutation of these residues is shown to select target RNA over an extended complementary sequence beyond guide-target duplex for RNA cleavage, establishing the functional significance of these hotspots. Collectively our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of highly selective RNA-based cleavage and detection tools.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

Mechanisms of DNA-Mediated Allostery

Proteins often regulate their activities via allostery-or action at a distance-in which the binding of a ligand at one binding site influences the affinity for another ligand at a distal site. Although less studied than in proteins, allosteric effects have been observed in experiments with DNA as well. In these experiments two or more proteins bind at distinct DNA sites and interact indirectly with each other, via a mechanism mediated by the linker DNA molecule. We develop a mechanical model of DNA/protein interactions which predicts three distinct mechanisms of allostery. Two of these involve an enthalpy-mediated allostery, while a third mechanism is entropy driven. We analyze experiments of DNA allostery and highlight the distinctive signatures allowing one to identify which of the proposed mechanisms best fits the data.

The Onset of Molecule-Spanning Dynamics in Heat Shock Protein Hsp90

Protein dynamics have been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how fast (local) fluctuations can lead to slow global (allosteric) changes. Here, fast molecule-spanning dynamics on the 100 to 200 ns time scale in the heat shock protein 90 (Hsp90) are shown. Global real-space movements are assigned to dynamic modes on this time scale, which is possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom molecular dynamics (MD) simulations. The time scale of these dynamic modes depends on the conformational state of the Hsp90 dimer. In addition, the dynamic modes are affected to various degrees by Sba1, a co-chaperone of Hsp90, depending on the location within Hsp90, which is in very good agreement with MD simulations. Altogether, this data is best described by fast molecule-spanning dynamics, which precede larger conformational changes in Hsp90 and might be the molecular basis for allostery. This integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities, making it difficult to specifically target one kinase, and allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss the methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or 'sensors,' are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Tracing a new path in the field of AI and robotics: mimicking human intelligence through chemistry. Part II: systems chemistry

Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström (10 - 10 meters) to the micrometer scales (10 - 6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of "general AI." These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities making it difficult to specifically target one kinase, allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or "sensors" are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Supramolecular multivalency effects enhance imine formation in aqueous medium allowing for dynamic modification of enzymatic activity

Imine formation under physiological conditions represents a challenging reaction due to the strong propensity of aldimines to be hydrolyzed. Herein we disclose the remarkable effect of supramolecular multivalency on increasing imine stability. A family of reactive aldehydes was synthesized bearing supramolecularly-active sites within their structure. The imine formation activity for such aldehydes was evaluated and compared with model aldehydes. The reaction of the best-performing species - containing two carboxylate groups-with a set of amines showed a significant decrease in imine yields as the degree of supramolecular multivalency between sidechains decreased. The reversible conjugation of amino acid derivatives and small peptides was also assayed, with excellent selectivities for the imine formation at the Nα position even in substrates containing competing sites. Preliminary results on protein bioconjugation revealed that a model enzyme could be dynamically inhibited upon reaction with the aldehyde, with its native activity being recovered by displacing the imine bonds with a suitable chemical effector (i.e., acylhydrazide).

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling

G-protein coupled receptors (GPCRs) present specific activation pathways and signaling among receptor subtypes. Hence, an extensive knowledge of the structural dynamics of the receptor is critical for the development of therapeutics. Here, we target the adenosine A1 receptor (A1R), for which a negligible number of drugs have been approved. We combine molecular dynamics simulations, enhanced sampling techniques, network theory and pocket detection to decipher the activation pathway of A1R, decode the allosteric networks and identify transient pockets. The A1R activation pathway reveal hidden intermediate and pre-active states together with the inactive and fully-active states observed experimentally. The protein energy networks computed throughout these conformational states successfully unravel the extra and intracellular allosteric centers and the communication pathways that couples them. We observe that the allosteric networks are dynamic, being increased along activation and fine-tuned in presence of the trimeric G-proteins. Overlap of transient pockets and energy networks uncover how the allosteric coupling between pockets and distinct functional regions of the receptor is altered along activation. By an in-depth analysis of the bridge between activation pathway, energy networks and transient pockets, we provide a further understanding of A1R. This information can be useful to ease the design of allosteric modulators for A1R.

RNA-mediated Allosteric Activation in CRISPR-Cas13a

Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. The protein uses a CRISPR RNA (crRNA) to target RNA sequences, which are cleaved by a composite active site formed by two 'Higher Eukaryotes and Prokaryotes Nucleotide' (HEPN) catalytic domains. In this system, an intriguing form of allosteric communication controls RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory and RNA cleavage assays to decipher this activation mechanism. We show that the binding of a target RNA acts as an allosteric effector of the spatially distant HEPN catalytic cleft, by amplifying the allosteric signals over the dynamical noise, that passes through the buried HEPN interface. Critical residues in this region - N378, R973, and R377 - rearrange their interactions upon target RNA binding, and alter allosteric signalling. Alanine mutation of these residues is experimentally shown to select target RNA over an extended complementary sequence beyond guide-target duplex, for RNA cleavage. Altogether, our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of more selective RNA-based cleavage and detection tools.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study we employ this paradigm to answer a basic question: in enzyme superfamilies where the catalytic mechanism, active sites and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as the representatives of the conserved Protein Tyrosine Phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Insight into the Nucleotide Based Modulation of the Grp94 Molecular Chaperone Using Multiscale Dynamics

Grp94, an ER-localized molecular chaperone, is required for the folding and activation of many membrane and secretory proteins. Client activation by Grp94 is mediated by nucleotide and conformational changes. In this work, we aim to understand how microscopic changes from nucleotide hydrolysis can potentiate large-scale conformational changes of Grp94. We performed all-atom molecular dynamics simulations on the ATP-hydrolysis competent state of the Grp94 dimer in four different nucleotide bound states. We found that Grp94 was the most rigid when ATP was bound. ATP hydrolysis or nucleotide removal enhanced mobility of the N-terminal domain and ATP lid, resulting in suppression of interdomain communication. In an asymmetric conformation with one hydrolyzed nucleotide, we identified a more compact state, similar to experimental observations. We also identified a potential regulatory role of the flexible linker, as it formed electrostatic interactions with the Grp94 M-domain helix near the region where BiP is known to bind. These studies were complemented with normal-mode analysis of an elastic network model to investigate Grp94's large-scale conformational changes. SPM analysis identified residues that are important in signaling conformational change, many of which have known functional relevance in ATP coordination and catalysis, client binding, and BiP binding. Our findings suggest that ATP hydrolysis in Grp94 alters allosteric wiring and facilitates conformational changes.

Enhancement of SARS-CoV-2 receptor-binding domain activity by two microbial defensins

Peptide binders are of great interest to both basic and biomedical research due to their unique properties in manipulating protein functions in a precise spatial and temporal manner. The receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein is a ligand that captures human angiotensin-converting enzyme 2 (ACE2) to initiate infection. The development of binders of RBDs has value either as antiviral leads or as versatile tools to study the functional properties of RBDs dependent on their binding positions on the RBDs. In this study, we report two microbe-derived antibacterial defensins with RBD-binding activity. These two naturally occurring binders bind wild-type RBD (WT RBD) and RBDs from various variants with moderate-to-high affinity (7.6-1,450 nM) and act as activators that enhance the ACE2-binding activity of RBDs. Using a computational approach, we mapped an allosteric pathway in WT RBD that connects its ACE2-binding sites to other distal regions. The latter is targeted by the defensins, in which a cation-π interaction could trigger the peptide-elicited allostery in RBDs. The discovery of the two positive allosteric peptides of SARS-CoV-2 RBD will promote the development of new molecular tools for investigating the biochemical mechanisms of RBD allostery.